This invention relates to improvements in an apparatus which supervises the operation of a control elevator with a learning function.
Regarding the supervising operation of an elevator, there have heretofore been considered various measures in which future traffic are predicted on the basis of past traffic to control the elevator. For example, the simplest measure is such that at least one cage of the elevator is caused to wait at the ground level floor because the volume of traffic from this floor is usually large. To further reduce congestion, when this cage carries passengers to another floor, another cage is caused to stop at ground level. A similar measure is installed at any floor level having a similar traffic pattern. In a more complicated measure, a group supervisory control is carried out by calculating the estimated times at which a cage will reach respective floors, and then calculating the predictive waiting time intervals of hall calls at the respective floors.
Meanwhile, in recent years, supervision means called a "learning function", in which the statistics of elevator traffic conditions in the past are utilized to predict future traffic condition more precisely or a traffic of an elevator, has been proposed and two such proposals are disclosed in, e.g., the published Japanese Laid-open Patent Applications Nos. 55-115566 and 57-62179.
In view of the characteristics of the elevator traffic, however, the traffic predicted from the learning function is often inaccurate and inefficiently utilized. When the predicted traffic differs greatly from the actual traffic, call registration from users for requested floors is not serviced properly while services to other unrequested floors increase drastically due to allocation errors. On the other hand, when the predicted traffic based on the learning function agrees with the actual traffic, the overall service of the elevator is quickly enhanced. More specifically, erroneous detection of a traffic pattern is prevented when it is selected after some traffic jam has occurred, and rendered inactive upon lapse of a prescribed period of time. As a result, it is necessary to utilize the learning function for the supervision of the elevator in such a manner that erroneous prediction of future traffic is avoided.